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Computer Science > Information Theory

Title:Active Secure Coding Based on Eavesdropper Behavior Learning

Abstract: The secrecy capacity achieving problem of the wiretap channel against an
active eavesdropper with unlimited computational power over is an important
foresighted task for secure communication. For active wiretap channel, the
effectiveness of cryptography embedded secure coding schemes are limited due to
the passive problem of physical layer coding. Thus in this paper, a novel
solution called active secure coding scheme is proposed which combines physical
secure coding with machine learning to implement an active defense against the
active eavesdropper.
To construct an universal active method for secure coding, an abstract active
wiretap channel model is constructed under the detectable precondition, in
which hidden Markov model is employed to build the internal eavesdropper
behavior pattern (the stochastic process for eavesdropper behavior states) with
stochastically mapped external observations. Based on the abstract model, an
eavesdropper behavior pattern learning and eavesdropper behavior states
decoding are constructed for estimating the optimal eavesdropper behavior
states, which enables the secure coding scheme to respond the eavesdropper
actively. Then this active secure coding method is performed to the general
varying wiretap channel to construct an explicit active secure polar coding
scheme. As proofed, the proposed active secure polar coding scheme can
theoretically achieve the average secrecy capacity of $t$ times secure
transmissions under the reliability and strong security criterions.